The importance of interpretability was recognized by many researchers.
Several works present methods for explaining a decision
of a system that uses the embedding (mainly classifiers) such as [1]}, [2]}.
These methods are mainly tailored for understanding the decisions rather than
the model itself. Some works [3]}, [4]} try to solve the problem of model understanding by
training or retraining for generating a new model that is interpretable, thus detouring
the problem of understanding the original model.
Another line of work tries to find orthogonal transformations. It can help the interpretability but it provides a limited level of interpretabilty and usually requires an embedding matrix [5]}, [6]}.
Many methods try to assign meaning to each dimension of the embedding space.
For example, some probing methods use classification to find
meaning to the individual space dimensions [7]}, [8]}.
